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Author Correction: Genomic analysis of a large set of currently-and historically-important human adenovirus pathogens

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Emerging microbes & infections 2018年第1期7卷 208页

作者： Ashrafali M Ismail Tiange Cui Kalpana Dommaraju Gurdeep Singh Shoaleh Dehghan Jason Seto Susmita Shrivastava Nadia B Fedorova Neha Gupta Timothy B Stockwell Rebecca Halpin Ramana Madupu Albert Heim Adriana E Kajon Eric G Romanowski Regis P Kowalski Jambulingam Malathi Kuzhanthai L Therese Hajib Narahari Madhavan Qiwei Zhang Leonardo J Ferreyra Morris S Jones Jaya Rajaiya David W Dyer James Chodosh Donald Seto Department of Ophthalmology Howe Laboratory Massachusetts Eye and Ear Infirmary Harvard Medical School Boston MA 02114 USA. Bioinformatics and Computational Biology Program School of Systems Biology George Mason University Manassas VA 20110 USA. Chemistry Department American University Washington DC 20016 USA. J. Craig Venter Institute Rockville MD 20850 USA. Institut für Virologie Medizinische Hochschule Hannover Hannover 30625 Germany. Lovelace Respiratory Research Institute Albuquerque NM 87108 USA. Charles T. Campbell Ophthalmic Microbiology Laboratory Ear and Eye Institute School of Medicine University of Pittsburgh Pittsburgh PA 15213 USA. L & T Microbiology Research Center Kamalnayan Bajaj Research Center Sankara Nethralaya No. 41 College Road Chennai Tamil Nadu 600006 India. Biosafety Level-3 Laboratory School of Public Health Southern Medical University 510515 Guangzhou China. Institute of Virology "J. M. Vanella" Enfermera Gordillo Gómez s/n Agencia 4 Ciudad Universitaria CP 5016 Córdoba Argentina. School of Public Health University of California Berkeley Berkeley CA 94704 USA. Department of Microbiology and Immunology University of Oklahoma Health Sciences Center Oklahoma City OK 73104 USA. Department of Ophthalmology Howe Laboratory Massachusetts Eye and Ear Infirmary Harvard Medical School Boston MA 02114 USA. james_chodosh@meei.harvard.edu. Bioinformatics and Computational Biology Program School of Systems Biology George Mason University Manassas VA 20110 USA. dseto@gmu.edu.

The original publication of this article [1] was missing the below author that made contributions to the research and the published article.

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40. Genomic profiling of lung adenocarcinomas with increased MET copy numbers

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Cancer Genetics 2018年 226卷 51-51页

作者： Liu, Ching-Hua (Ruby) Chen, Hui Fang, Lianghua Hu, Peter C. Gu, Jun Zhao, Jin Abraham, Ronald Yao, Hui Singh, Rajesh R. Jeffrey Medeiros, L. Luthra, Rajyalakshimi Lu, Xinyan Department of Hematopathology University of Texas MD Anderson Cancer Center Houston TX United States Master of Science in Diagnostic Genetics Program School of Health Professions University of Texas MD Anderson Cancer Center Houston TX United States Department of Pathology Northwestern University Feinberg School of Medicine Chicago IL United States Department of Pathology University of Texas MD Anderson Cancer Center Houston TX United States Department of Oncology Jiangsu Hospital of Traditional Chinese Medicine Nanjing Jiangsu China Department of Clinical Laboratory Hunan Cancer Hospital and The Affiliated Cancer Hospital of Xiangya School of Medicine Central South University Shangsha Hunan China Department of Bioinformatics and Computational Biology University of Texas MD Anderson Cancer Center Houston TX United States

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P065 Information theory-based analysis of classical HLA genes

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Human Immunology 2016年 77卷 85-85页

作者： Hu Huang Wei Wang Yung-Tsi Bolon Craig Malmberg Caleb Kennedy Martin Maiers University of Minnesota Bioinformatics and Computational Biology Minneapolis MN United States National Marrow Donor Program Bioinformatics Research Minneapolis MN United States

Aim In IMGT/HLA Release 3.24.0, less than 7% of the named classical HLA alleles have curated full-length gene sequences or sequences outside the antigen recognition sites (ARS). This study is to investigate genetic variants, polymorphism, SNPs and linkage disequilibrium (LD) within the entire gene region for the classical HLA genes using information theory. Methods Multiple sequence alignment was performed on full-length HLA Class I alleles (HLA-A, -B, -C) and partial-length (from intron 1 through intron 3) Class II alleles (HLA-DQB1, -DRB1) for 15,865 subjects, followed by a gene annotation pipeline. We analyzed the Shannon entropy of each nucleotide site and mutual information within each of the loci. Conditional asymmetric linkage disequilibrium (ALD) measures were employed to characterize the LD between multiallelic SNPs within a locus. Results For HLA Class I alleles, the untranslated regions generally show high entropy variation. Highly variable sites (entropy >0.01) appear most in the noncoding regions (intron 3 in HLA-A and -B, respectively; intron 4 in HLA-C); while within coding regions, the ARS regions contain the most variants. However, since multiple sequence alignment introduces gaps that are ambiguous, we considered the distribution of variants after removing sites with at least one alignment gap. Consequently, for noncoding regions, intron 5 in HLA-A and -B, respectively, and introns 3 and 4 in HLA-C show the highest entropy variation; for coding regions, ARS regions remain highest entropy variation across all five loci. The ALD measures indicate that highly associated polymorphic sites mostly reside within exons 2, 3 and 4 (ARS regions for class II). Some of these associations also showed high heterogeneity. Conclusions Non-coding regions along with the non-ARS coding regions include many informative sites and the proposed method could be utilized in allele typing, typing validation, SNP association study, as well as matching for stem cell transplan

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Author Correction: Multi-ancestry genome-wide association analyses improve resolution of genes and pathways influencing lung function and chronic obstructive pulmonary disease risk

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Nature genetics 2023年第10期55卷 1778-1779页

作者： Nick Shrine Abril G Izquierdo Jing Chen Richard Packer Robert J Hall Anna L Guyatt Chiara Batini Rebecca J Thompson Chandan Pavuluri Vidhi Malik Brian D Hobbs Matthew Moll Wonji Kim Ruth Tal-Singer Per Bakke Katherine A Fawcett Catherine John Kayesha Coley Noemi Nicole Piga Alfred Pozarickij Kuang Lin Iona Y Millwood Zhengming Chen Liming Li Sara R A Wijnant Lies Lahousse Guy Brusselle Andre G Uitterlinden Ani Manichaikul Elizabeth C Oelsner Stephen S Rich R Graham Barr Shona M Kerr Veronique Vitart Michael R Brown Matthias Wielscher Medea Imboden Ayoung Jeong Traci M Bartz Sina A Gharib Claudia Flexeder Stefan Karrasch Christian Gieger Annette Peters Beate Stubbe Xiaowei Hu Victor E Ortega Deborah A Meyers Eugene R Bleecker Stacey B Gabriel Namrata Gupta Albert Vernon Smith Jian'an Luan Jing-Hua Zhao Ailin F Hansen Arnulf Langhammer Cristen Willer Laxmi Bhatta David Porteous Blair H Smith Archie Campbell Tamar Sofer Jiwon Lee Martha L Daviglus Bing Yu Elise Lim Hanfei Xu George T O'Connor Gaurav Thareja Omar M E Albagha Karsten Suhre Raquel Granell Tariq O Faquih Pieter S Hiemstra Annelies M Slats Benjamin H Mullin Jennie Hui Alan James John Beilby Karina Patasova Pirro Hysi Jukka T Koskela Annah B Wyss Jianping Jin Sinjini Sikdar Mikyeong Lee Sebastian May-Wilson Nicola Pirastu Katherine A Kentistou Peter K Joshi Paul R H J Timmers Alexander T Williams Robert C Free Xueyang Wang John L Morrison Frank D Gilliland Zhanghua Chen Carol A Wang Rachel E Foong Sarah E Harris Adele Taylor Paul Redmond James P Cook Anubha Mahajan Lars Lind Teemu Palviainen Terho Lehtimäki Olli T Raitakari Jaakko Kaprio Taina Rantanen Kirsi H Pietiläinen Simon R Cox Craig E Pennell Graham L Hall W James Gauderman Chris Brightling James F Wilson Tuula Vasankari Tarja Laitinen Veikko Salomaa Dennis O Mook-Kanamori Nicholas J Timpson Eleftheria Zeggini Josée Dupuis Caroline Hayward Ben Brumpton Claudia Langenberg Stefan Weiss Georg Homuth Carsten Oliver Schmidt Nicole Probst-Hensch Marjo-Riitta Jarvelin Alanna C Morrison Ozren Polasek Igor Department of Population Health Sciences University of Leicester Leicester UK. nick.shrine@leicester.ac.uk. Department of Population Health Sciences University of Leicester Leicester UK. Division of Respiratory Medicine and NIHR Nottingham Biomedical Research Centre University of Nottingham Nottingham UK. Leicester National Institute for Health and Care Research Biomedical Research Centre Glenfield Hospital Leicester UK. Channing Division of Network Medicine Division of Pulmonary and Critical Care Medicine Department of Medicine Brigham and Women's Hospital Boston MA USA. Harvard Medical School Boston MA USA. COPD Foundation Washington DC USA. Department of Clinical Science Unversity of Bergen Bergen Norway. Nuffield Department of Population Health University of Oxford Oxford UK. MRC Population Health Research Unit University of Oxford Oxford UK. Department of Epidemiology and Biostatistics School of Public Health Peking University Health Science Center Beijing China. Department of Respiratory Diseases Ghent Universital Hospital Ghent Belgium. Department of Bioanalysis Faculty of Pharmaceutical Sciences Ghent University Ghent Belgium. Department of Epidemiology Eramus Medical Center Rotterdam The Netherlands. Department of Internal Medicine Eramus Medical Center Rotterdam The Netherlands. Center for Public Health Genomics University of Virginia Charlottesville VA USA. Department of Medicine Columbia University Medical Center New York NY USA. Medical Research Council Human Genetics Unit Institute of Genetics and Cancer University of Edinburgh Edinburgh UK. Human Genetics Center Department of Epidemiology Human Genetics and Environmental Sciences School of Public Health The University of Texas Health Science Center at Houston Houston TX USA. MRC Centre for Environment and Health Department of Epidemiology and Biostatistics School of Public Health Imperial College London London UK. Department of Epidemiology and Public Health Swiss Tropical

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Publisher Correction: Deep coverage whole genome sequences and plasma lipoprotein(a) in individuals of European and African ancestries

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Nature communications 2020年第1期11卷 1715页

作者： Seyedeh M Zekavat Sanni Ruotsalainen Robert E Handsaker Maris Alver Jonathan Bloom Timothy Poterba Cotton Seed Jason Ernst Mark Chaffin Jesse Engreitz Gina M Peloso Ani Manichaikul Chaojie Yang Kathleen A Ryan Mao Fu W Craig Johnson Michael Tsai Matthew Budoff Ramachandran S Vasan L Adrienne Cupples Jerome I Rotter Stephen S Rich Wendy Post Braxton D Mitchell Adolfo Correa Andres Metspalu James G Wilson Veikko Salomaa Manolis Kellis Mark J Daly Benjamin M Neale Steven McCarroll Ida Surakka Tonu Esko Andrea Ganna Samuli Ripatti Sekar Kathiresan Pradeep Natarajan Program in Medical and Population Genetics Broad Institute of MIT and Harvard Cambridge MA 02142 USA. Yale School of Medicine New Haven CT 06510 USA. Department of Computational Biology & Bioinformatics Yale University New Haven CT 06510 USA. Institute for Molecular Medicine University of Helsinki Helsinki Finland. Stanley Center for Psychiatric Research Broad Institute of MIT and Harvard Cambridge MA 02142 USA. Department of Genetics Harvard Medical School Boston MA 02115 USA. Department of Biotechnology Institute of Molecular and Cell Biology University of Tartu Tartu Estonia. Estonian Genome Center Tallinn Estonia. Analytic and Translational Genetics Unit Boston MA 02142 USA. Department of Biological Chemistry University of California Los Angeles Los Angeles CA 90095 USA. Department of Biostatistics Boston University School of Public Health Boston MA 02118 USA. Center for Public Health Genomics University of Virginia Charlottesville VA 22904 USA. Program in Personalized and Genomic Medicine Division of Endocrinology Diabetes & Nutrition Department of Medicine University of Maryland School of Medicine Baltimore MD 21201 USA. Department of Biostatistics School of Public Health and Community Medicine University of Washington Seattle WA 98195 USA. Department of Laboratory Medicine and Pathology University of Minnesota Minneapolis MN 55455 USA. Division of Cardiology Harbor-UCLA Medical Center Los Angeles Biomedical Research Institute Los Angeles CA 90509 USA. NHLBI Framingham Heart Study Framingham MA 20892 USA. Sections of Preventive medicine and Epidemiology and cardiovascular medicine Departments of Medicine and Epidemiology Boston university Schools of Medicine and Public health Boston MA 02118 USA. Departments of Pediatrics and Medicine The Institute for Translational Genomics and Population Sciences Los Angeles Biomedical Research Institute Harbor-UCLA Medical Center Torrance CA 90509 USA. Division of Cardiology

An amendment to this paper has been published and can be accessed via a link at the top of the paper.

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Random close packing in protein cores

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Physical Review E 2016年第3期93卷 032415-032415页

作者： Jennifer C. Gaines W. Wendell Smith Lynne Regan Corey S. O'Hern Program in Computational Biology and Bioinformatics Yale University New Haven Connecticut 06520 USA Integrated Graduate Program in Physical and Engineering Biology (IGPPEB) Yale University New Haven Connecticut 06520 USA Department of Physics Yale University New Haven Connecticut 06520 USA Department of Molecular Biophysics & Biochemistry Yale University New Haven Connecticut 06520 USA Department of Chemistry Yale University New Haven Connecticut 06520 USA Department of Mechanical Engineering & Materials Science Yale University New Haven Connecticut 06520 USA Department of Applied Physics Yale University New Haven Connecticut 06520 USA

Shortly after the determination of the first protein x-ray crystal structures, researchers analyzed their cores and reported packing fractions ϕ≈0.75, a value that is similar to close packing of equal-sized spheres. A limitation of these analyses was the use of extended atom models, rather than the more physically accurate explicit hydrogen model. The validity of the explicit hydrogen model was proved in our previous studies by its ability to predict the side chain dihedral angle distributions observed in proteins. In contrast, the extended atom model is not able to recapitulate the side chain dihedral angle distributions, and gives rise to large atomic clashes at side chain dihedral angle combinations that are highly probable in protein crystal structures. Here, we employ the explicit hydrogen model to calculate the packing fraction of the cores of over 200 high-resolution protein structures. We find that these protein cores have ϕ≈0.56, which is similar to results obtained from simulations of random packings of individual amino acids. This result provides a deeper understanding of the physical basis of protein structure that will enable predictions of the effects of amino acid mutations to protein cores and interfaces of known structure.

关键词： Protein structure Proteins

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Stroke genetics informs drug discovery and risk prediction across ancestries (vol 611, pg 115, 2022)

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NATURE 2022年第7938期612卷 E7-E7页

作者： Mishra, Aniket Malik, Rainer Hachiya, Tsuyoshi Jurgenson, Tuuli Namba, Shinichi Posner, Daniel C. Kamanu, Frederick K. Koido, Masaru Le Grand, Quentin Shi, Mingyang He, Yunye Georgakis, Marios K. Caro, Ilana Krebs, Kristi Liaw, Yi-Ching Vaura, Felix C. Lin, Kuang Winsvold, Bendik Slagsvold Srinivasasainagendra, Vinodh Parodi, Livia Bae, Hee-Joon Chauhan, Ganesh Chong, Michael R. Tomppo, Liisa Akinyemi, Rufus Roshchupkin, Gennady V. Habib, Naomi Jee, Yon Ho Thomassen, Jesper Qvist Abedi, Vida Carcel-Marquez, Jara Nygaard, Marianne Leonard, Hampton L. Yang, Chaojie Yonova-Doing, Ekaterina Knol, Maria J. Lewis, Adam J. Judy, Renae L. Ago, Tetsuro Amouyel, Philippe Armstrong, Nicole D. Bakker, Mark K. Bartz, Traci M. Bennett, David A. Bis, Joshua C. Bordes, Constance Borte, Sigrid Cain, Anael Ridker, Paul M. Cho, Kelly Chen, Zhengming Cruchaga, Carlos Cole, John W. de Jager, Phil L. de Cid, Rafael Endres, Matthias Ferreira, Leslie E. Geerlings, Mirjam I. Gasca, Natalie C. Gudnason, Vilmundur Hata, Jun He, Jing Heath, Alicia K. Ho, Yuk-Lam Havulinna, Aki S. Hopewell, Jemma C. Hyacinth, I. Hyacinth Inouye, Michael Jacob, Mina A. Jeon, Christina E. Jern, Christina Kamouchi, Masahiro Keene, Keith L. Kitazono, Takanari Kittner, Steven J. Konuma, Takahiro Kumar, Amit Lacaze, Paul Launer, Lenore J. Lee, Keon-Joo Lepik, Kaido Li, Jiang Li, Liming Manichaikul, Ani Markus, Hugh S. Marston, Nicholas A. Meitinger, Thomas Mitchell, Braxton D. Montellano, Felipe A. Morisaki, Takayuki Mosley, Thomas H. Nalls, Mike A. Nordestgaard, Borge G. O'Donnell, Martin J. Okada, Yukinori Onland-Moret, N. Charlotte Ovbiagele, Bruce Peters, Annette Psaty, Bruce M. Rich, Stephen S. Rosand, Jonathan Sabatine, Marc S. Sacco, Ralph L. Saleheen, Danish Sandset, Else Charlotte Salomaa, Veikko Sargurupremraj, Muralidharan Sasaki, Makoto Satizabal, Claudia L. Schmidt, Carsten O. Shimizu, Atsushi Smith, Nicholas L. Sloane, Kelly L. Sutoh, Yoichi Sun, Yan V. Tanno, Kozo Tiedt, Steffen Tatlisumak, Turgut Torres-Aguila, Nuria P. Tiwari, Hemant K. Tregouet, Bordeaux Population Health Research Center University of Bordeaux Inserm UMR 1219 Bordeaux France Department of Public Health Bordeaux University Hospital Bordeaux France Department of Neurology Memory Clinic Bordeaux University Hospital Bordeaux France Department of Neurology Institute for Neurodegenerative Diseases CHU de Bordeaux Bordeaux France Institute for Stroke and Dementia Research (ISD) University Hospital LMU Munich Munich Germany Center for Genomic Medicine Massachusetts General Hospital Boston MA USA Program in Medical and Population Genetics Broad Institute of Harvard and the Massachusetts Institute of Technology Cambridge MA USA Munich Cluster for Systems Neurology Munich Germany German Center for Neurodegenerative Diseases (DZNE) Munich Germany Iwate Tohoku Medical Megabank Organization Iwate Medical University Iwate Japan Estonian Genome Centre Institute of Genomics University of Tartu Tartu Estonia Institute of Mathematics and Statistics University of Tartu Tartu Estonia Department of Computational Biology University of Lausanne Lausanne Switzerland Swiss Institute of Bioinformatics Lausanne Switzerland University Center for Primary Care and Public Health Lausanne Switzerland Department of Statistical Genetics Osaka University Graduate School of Medicine Suita Japan Department of Genome Informatics Graduate School of Medicine The University of Tokyo Tokyo Japan Laboratory for Systems Genetics RIKEN Center for Integrative Medical Sciences Yokohama Japan Laboratory of Statistical Immunology Immunology Frontier Research Center (WPI-IFReC) Osaka University Suita Japan Integrated Frontier Research for Medical Science Division Institute for Open and Transdisciplinary Research Initiatives Osaka University Suita Japan Center for Infectious Disease Education and Research (CiDER) Osaka University Suita Japan Massachusetts Veterans Epidemiology Research and Information Center (MAVERIC) VA Boston Healthcare System Boston MA USA TIMI S

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MOST+: A de novo motif finding approach combining genomic sequence and heterogeneous genome-wide signatures

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BMC Genomics 2015年第7期16卷 1-12页

作者： Zhang, Yizhe He, Yupeng Zheng, Guangyong Wei, Chaochun Shanghai Jiao Tong University School of Life Sciences and Biotechnology 800 Dongchuan Road Shanghai 200240 China Shanghai Center for Bioinformation Technology 1278 Keyuan Road Pudong District Shanghai 201203 China University of California Bioinformatics and Systems Biology Program San Diego 9500 Gilman Drive La Jolla 92093 CA United States Shanghai Institutes for Biological Sciences Chinese Academy of Sciences CAS-MPG Partner Institute for Computational Biology 320 Yueyang Road Shanghai 200031 China Duke University Program in Computational Biology and Bioinformatics Durham 27708 NC United States

Background: Motifs are regulatory elements that will activate or inhibit the expression of related genes when proteins (such as transcription factors, TFs) bind to them. Therefore, motif finding is important to understand the mechanisms of gene regulation. De novo discovery of regulatory elements, like transcription factor binding sites (TFBSs), has long been a major challenge to gain insight on mechanisms of gene regulation. Recent advances in experimental profiling of genome-wide signals such as histone modifications and DNase I hypersensitivity sites allow scientists to develop better computational methods to enhance motif discovery. However, existing methods for motif finding suffer from high false positive rates and slow speed, and it's difficult to evaluate the performance of these methods systematically. Result: Here we present MOST+, a motif finder integrating genomic sequences and genome-wide signals such as intensity and shape features from histone modification marks and DNase I hypersensitivity sites, to improve the prediction accuracy. MOST+ can detect motifs from a large input sequence of about 100 Mbs within a few minutes. Systematic comparison method has been established and MOST+ has been compared with existing methods. Conclusion: MOST+ is a fast and accurate de novo method for motif finding by integrating genomic sequence and experimental signals as clues. © 2015 Zhang et al.;licensee BioMed Central Ltd.

关键词： ChIP-seq De novo Motif finding DNase I hypersensitivity sites Epigenetics Suffix tree

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Super deduper, fast PCR duplicate detection in fastq files. 15

Super deduper, fast PCR duplicate detection in fastq files.

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6th ACM Conference on bioinformatics, computational biology, and Health Informatics, BCB 2015

作者： Petersen, Kristen R. Streett, David A. Gerritsen, Alida T. Hunter, Samuel S. Settles, Matthew L. Program in Bioinformatics and Computational Biology University of Idaho MoscowID83844-3051 United States Institute for Bioinformatics and Evolutionary Studies University of Idaho MoscowID83844-3051 United States Department of Biological Sciences University of Idaho MoscowID83844-3051 United States

ISBN: (纸本)9781450338530

Our goal was to explore the accuracy and utility of identifying and removing PCR duplicates from HTS data using Super Deduper. Super Deduper is a pre-alignment, sequence read based technique developed at the University of Idaho, which examines and uses only a small portion of each read's sequence in order to identify and remove PCR and/or optical duplicates. Through comparisons with well-known pre and post-alignment techniques, Super Deduper's parameters were optimized and its performance assessed. The results conclude that Super Deduper is a viable prealignment alternative to post-alignment techniques. Super Deduper is both independent of a reference genome and choice in alignment application, allowing for its use in a greater variety of HTS applications. Super Deduper is an open source application and can be found at https://***/dstreett/Super-Deduper. Copyright 2015 ACM.

关键词： Alignment

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ExpHTS: Analysis of high throughput sequence data in an experimental framework 15

ExpHTS: Analysis of high throughput sequence data in an expe...

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6th ACM Conference on bioinformatics, computational biology, and Health Informatics, BCB 2015

作者： Streett, David A. Petersen, Kristen R. Gerritsen, Alida T. Hunter, Samuel S. Settles, Matthew L. Program in Bioinformatics and Computational Biology University of Idaho MoscowID83844-3051 United States Institute for Bioinformatics and Evolutionary Studies University of Idaho MoscowID83844-3051 United States Department of Biological Sciences University of Idaho MoscowID83844-3051 United States

ISBN: (纸本)9781450338530

Analysis time and storage space are critical bottlenecks when analyzing multi-sample, comparative high throughput sequence experiments. The experimental High Throughput Sequencing (expHTS) pipeline mitigates these bottlenecks by streaming external application data, speeding analysis, and reducing storage space by eliminating intermediate, un-analyzed files. This creates faster run times, less storage space on the machine, and improves uniformity of sample analysis. Copyright 2015 ACM.

关键词： Pipelines

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